Profile of ocular injuries amongst various trauma patients presenting to tertiary care centre of Punjab

To study the epidemiology and clinical profile of ocular trauma patients presenting to tertiary care centre. Prospective study. All patients of ocular trauma in OPD/Emergency were assessed for detail between 1/1/17 to 31/6/18 and data on demographic profile was established as per guidelines of Ocular Trauma Society of India. Patients were categorized in different segments and assessed/followed for visual impairment in particular. A total of 246 cases were examined out of which 87% were males. The most common mode of ocular injury was Road Traffic Accidents. Pediatric eye trauma constituted 16.7% of the total cases. 26.8% cases arrived to our centre between 4-24 hours and 62.6% cases presented after 24 hours. Amongst 131 cases of Road Traffic Accidents, none of them were using protective measures like helmets or goggles. Of these, 17.1% were under the influence of alcohol. 28.5% were involved in medicolegal proceedings. Majority of the cases comprised of monocular trauma (78.1%). Closed globe injuries constituted 88.94% of the total cases of which most cases presented with lid edema and ecchymosis. Chemical injuries were reported in 4.5% cases. 9 patients lost vision completely and 71 cases had vision from light perception to 6/18. Ocular trauma is one of the common causes of ocular morbidity. It has been seen predominantly in male population. Public needs to be educated about safety measurements and education about prompt need to specialised care to reduce ocular trauma related visual morbidity.

Epidemiology of surgically managed traumatic brain injury patients

Traumatic brain injury (TBI) constitutes a major health and socioeconomic problem throughout the world TBI is called the ‘silent epidemic’ because problems resulting from TBI are often not immediately visible and TBI patients are not very vociferous. Epidemiological studies of TBI are essential to the targeted prevention and effective treatment of brain-injured patients. Epidemiology analysis of surgically managed traumatic brain injury patients was done. Mean age was 35.9 years. Males were more commonly (80%) involved than females (20%). In 57.5% of cases, falls were responsible for TBI and in 42.5% of cases, Road traffic accidents were responsible. Edh was the most common type of TBI in (50%). Chronic SDH occurred in 25% of cases. Acute SDH and Contusions were both seen in 13.75% of cases. Depressed fractures occurred in 6.25% of cases and ICH occurred in 1.25% of cases. Craniotomy was the most common (42%) surgical procedure performed, followed by burrhole drainage (22.5%). Decompressive craniectomy was done in 18.75% of cases and elevation of depressed fracture was performed in 6.25% of cases. Traumatic brain injury (TBI) constitutes a major health and socioeconomic problem throughout the world. People of all ages are affected by it. Males are more commonly involved as compared to females. Timely hospitalisation and surgical management whenever indicated improves the survival.

Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

Intelligent connected mobility 4.0: Regulatory concepts

The last decade reflects undeniable rapid growth in intelligent connected mobility in the European Union and internationally. Whereas automotive producers united forces to address the projected technical difficulties vis-à-vis the deployment of Intelligent Connected Vehicles through coordinated efforts and partnerships, academia is committed to clarifying fundamental new regulatory concepts to reveal potential and foreseeable legal inconsistencies in such technological development. The lack of a determination of the fundamental legal concepts or the vague and ambiguous determination of essential regulatory concepts creates overall legal uncertainty and is considered an obstacle to ensuring the smooth market penetration of Intelligent Connected Vehicles in the European Union. This article claims its contribution to existing literature by integrating further unambiguous and specific regulatory concepts in the context of the regulation of Intelligent Connected Vehicles. This article addresses: (i) the prerequisites for uniform Intelligent Connected Vehicles’ fundamental regulatory concepts based on complex retrospective analysis vis-à-vis road traffic accidents involving conventional vehicles and (ii) the prototype of regulatory concepts that need to be established and accurately distinguished for intelligent connected mobility 4.0, with the cross-border element at the European Union level.

COMPARATIVE ANALYSIS OF ROAD TRANSPORTATION INCIDENTS IN IRKUTSK REGION AND RUSSIA

The article analyzes the indicators of road traffic accidents for 2020 in the Irkutsk region and Russia. The main reasons for the implementation of road accidents are identified. The current state of road safety has been analyzed within the framework of the Safe and High-Quality Roads Project. An excess of the actual values of social and transport risks was established by 1.19 and 1.38 times the corresponding Russian indicators

Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.

Analytical report on post traumatic hydrocephalus

Post-traumatic hydrocephalus (PTH) is a field and disorder less explored in neurosurgery though we see many cases. The commonest causes in our set up includes head injury for which people have undergone decompressive craniectomies, severe head injuries with raised ICP. We did a clinical analysis on 23 cases in a period of 3 years duration from 2018 -2021. We did Evd in few cases for emergency purposes when there was decerebration and we went ahead with VP shunt in all the patients who had gross ventricular dilatation. We have projected our analytical report in these cases. METHODS A retrospective study was conducted in the Department of Neurosurgery in Sri Ramachandra medical college. The clinical outcome of patients diagnosed with PTH was studied. These cases were treated by surgery. The stastical analysis along with cause of the hydrocephalus with the outcome in pre and postoperative period were studied. RESULTS Among the 23 patients studied 82% were males. Road traffic accident was the main cause of injury. The other main cause was a fall from height. Assault was another reason for head injuries which we recorded. We found all road traffic accidents were only because of bike riders either pillion or the people driving the vehicle. . Craniotomy was done in 50 % of the patients, 90 % of the patients recovered who had a gcs of 13 to 7. People with gcs lss than 7 were intubated recovery rate was 7.5 %. . There was 100%mortality because of primary head injury in all the patients who had brain stem contusions with dilated pupil. CONCLUSIONS Trauma to head and who were operated had the highest incidence of post tramatic head injury. Smaller the decompressive craniectomies had symptomatic post traumatic head ache with post traumatic hydrocephalus.. CT scan of the brain is considered the choice of investigation toearly diagnose PTH.we even analysed the ct scan and found when there was periventricular lucency the patient outcome after VP shunting is good. KEY WORDS Hydrocephalus, Head Injury, Trauma

Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

Optimization of Emergency Transportation Organization of Holiday Tourism Traffic

The purpose of the optimization of holiday traffic emergency traffic organization is to solve the problem of serious traffic jams in holiday scenic spots. Based on the prediction of traffic volume and traffic mode division in the future years of the scenic spot, the traffic accident route is analyzed to provide theoretical support for the emergency traffic organization and planning of the scenic spot. This article takes the Shijiazhuang Jinta Bay scenic area as the research object, based on the traffic volume of the Jinta Bay tourist scenic area from 2009 to 2016, analyzes the traffic environment of the scenic area, predicts the traffic demand, and builds a one-way traffic organization double-layer optimization model. The simulated annealing algorithm is used to solve the model, an emergency transportation organization optimization plan is formulated, and the feasibility of the plan is verified through VISSIM simulation. The results of the study show that the one-way traffic organization method reduces the average vehicle delay by 32.2% and the average queue length by 14.5%. The one-way traffic organization based on branch diversion can more effectively solve the main road jamming and congestion caused by traffic accidents, prevent the occurrence of secondary accidents, and reduce the economic losses of scenic area managers. At the same time, the purpose of ensuring the tourist quality of tourists and the economic interests of scenic spot management departments is ensured.